Many of the power plants in operation today are more than 20-30 years old. During that time many changes have occurred in the plant and to plant equipment. Devices have degraded and often times been overhauled and modified mechanically. Moreover, a large number of the utility generation device burn fuels which significantly differ from the fuels for which the devices were designed to burn. As a result, the original manufacturer design curves that were developed at the time the devices were designed and installed in the power plants no longer represent the present-day operating capabilities of the devices.
The current method of performance monitoring for power plants and the devices therein was developed over 20-30 years ago for units operating with the expected conditions of the power industry at that time. The methodology then and now corresponds to the American and Western European standards of the 1960s and 1970s that emphasized reliability. At the time the methodology was developed, the methods brought many significant advantages in the form of improved quality of performance monitoring and control. However, the method is outdated by the current dynamic deregulation aspects of the power generation industry. Several basic factors contribute to the method becoming outdated. First, the advancement of computer technology that allows for the common use of digital automatic control systems. Secondly, system changes in the power energy market have made the efficacy of this method questionable under current operating conditions. Additionally, the availability of lower cost, highly precise instrumentation that is constantly being monitored and archived to a plant historian provide new opportunities for performance monitoring and comparison of the performance to best achievable performance rather than performance that may have been achievable when the power plant was constructed.
Digital automatic control systems have made constant control of performance parameters possible by assigning all parameters (and losses) on-line and permitting direct operator supervision. The increased quality of measurement devices and tools has reduced the role of periodic heat rate testing and warranty measurements. In addition, the high quality of DCS automated control connected with the increasingly common application of optimization systems (e.g., supervisory substitution or added bias signals to the operator actions during normal unit operation) has reduced the possibility of a simple improvement based on efficiency indexes. For this reason, the principle role of performance monitoring should be modified to compare actual performance to the best achievable performance for a device or process rather than the predicted performance based on the manufacturer design values, and to detect possible losses of running the in market-based generation dispatch. This change would be more meaningful and understandable to plant operations and engineering personnel.
The typical methodology of performance control is presented in numerous conference materials and textbooks. In short, the method is based on calculating the unit chemical energy usage rate, also known as unit heat rate (based on ASME Power Test Codes), and assigning the measured losses and deviations of the heat rate from the expected value (nominal, or resulting from the last warranty measurements) based on operation of the device at other than nominal conditions. The basic parameters that influence the unit heat rate and that may be taken into consideration include the main steam pressure, main steam temperature, pressure decrease in the super-heater, reheat steam temperature, condenser pressure, feed-water temperature, oxygen content in flue gas and stack gas temperature. The number of controlled parameters has been expanded many times, but does not change the primary theoretical basis of this method. The heat rate deviation (BTU/kWh) is usually calculated to a value of $/Hr for a more approachable and meaningful presentation of data. Systems based on ASME or similar methodologies were introduced in practically all power plants, with modernization of automatic control systems usually developed into on-line systems performing all the calculations every several minutes and presenting the results on operators' screens at the Distributed Control System (DCS) or auxiliary computer displays.
The performance calculation methodology is necessary and effective when properly implemented, but also has a set of drawbacks. It is apparent after many years, and many computing platform revisions to calculate results, it may be possible to evaluate the results more critically and to attempt a more in depth analysis. Some of the problems with applying the contemporary performance control techniques relate to the reference values and correction curves used in the control method. Presently, most deviations and losses are calculated and monitored in reference to the so-called reference values. Usually these are the nominal values given by the original equipment manufacturer (OEM). However, for devices with a 15-40 year life cycles and with equipment that has been modernized and rebuilt at least several times, these nominal values do not constitute a real reflection of the actual operating parameters of the device in its present configuration.
Problems also arise from the correction curves used for defining the controlled or measured losses of the devices. In the present performance monitoring methods, the influence of operational parameter deviations, such as main steam temperature, main steam pressure, and the like, from the design values (i.e. achievable, design, theoretical) are assigned largely using the so-called manufacturers' correction curves. Leaving the accuracy of these curves and the common problems with obtaining this data aside, the basis of his theory is to define the influence of these parameters (xi) (gradient) into unit heat rate (qb)-∂qb/∂xi. The manufacturer's data normally does not correspond to the real, dynamic operation of a modernized unit. At the same time, there appears to be a theoretical problem with assigning the deviation for the given control value. In the case of building a correction curve, it is assumed that a clear assignment of the influence of a given value onto unit heat rate will be possible (qb). In other words, variables such as pressure and temperature are treated as independent variables which finally leads to obtaining a dependence ∂qb/∂xi=f(xi). This results from, among other factors, the method of assigning correction curves through balance calculations and the change of an individual parameter in simulation calculations.
In actual practice, a strong relationship exists between these parameters during normal operation, and the parameters are interrelated. The relationships can be derived by utilizing statistical techniques. During normal operations, it is not possible to change one parameter without modifying others. Additionally, assigning relationships between these parameters is not only dependent on the thermodynamic dependencies (balance) but it is also influenced by the operation of the automatic control system controlling the unit. In other words, in practice when changing one of the main unit operational parameters, the automatic control systems perform a shift of the unit status into a different operating point, thus modifying the other parameters. Because of this, deviations assigned using correction curves cease to have any practical significance. For example, at a given moment deviations of a unit heat rate for a series of main parameters are assigned, and a negative deviation for one of the parameters resulting from the difference between the current and the nominal or reference value may be obtained. Canceling this difference by bringing the parameter to the nominal or reference value and thus reducing the deviation while the other parameters remain unchanged results in an entirely different system of parameters and differences of the parameters from reference values, and potentially new deviations in their values from the reference values where deviations did not previously exist.
Consequently, a need exists for using statistical data based analysis and control of the present-day operating conditions to determine the achievable and statistically controllable performance of thermodynamic devices and processes and to improve on the currently applied systems for performance monitoring by taking into account the statistically achievable performance rather than a theoretical or designed ideal performance level.
One specific example of an application where improved performance monitoring may benefit thermodynamic devices or processes is in fuel burning boilers where soot blowing is performed to adjust the efficiency of heat transfer within the boilers. A variety of industrial as well as non-industrial applications use fuel burning boilers, typically for converting chemical energy into thermal energy by burning one of various types of fuels, such as coal, gas, oil, waste material, etc. An exemplary use of fuel burning boilers is in thermal power generators, wherein fuel burning boilers are used to generate steam from water traveling through a number of pipes and tubes in the boiler and the steam is then used to generate electricity in one or more turbines. The output of a thermal power generator is a function of the amount of heat generated in a boiler, wherein the amount of heat is determined by the amount of fuel that can be burned per hour, etc. Additionally, the output of the thermal power generator may also be dependent upon the heat transfer efficiency of the boiler used to burn the fuel.
Burning of certain types of fuel, such as coal, oil, waste material, etc., generates a substantial amount of soot, slag, ash and other deposits (generally referred to as “soot”) on various surfaces in the boilers, including the inner walls of the boiler as well as on the exterior walls of the tubes carrying water through the boiler. The soot deposited in the boiler has various deleterious effects on the rate of heat transferred from the boiler to the water, and thus on the efficiency of any system using such boilers. It is necessary to address the problem of soot in fuel burning boilers that burn coal, oil, and other such fuels that generate soot in order to maintain a desired efficiency within the boiler. While not all fuel burning boilers generate soot, for the remainder of this patent, the term “fuel burning boilers” is used to refer to those boilers that generate soot.
Various solutions have been developed to address the problems caused by the generation and presence of soot deposits in boilers of fuel burning boilers. One approach is the use of soot blowers to remove soot encrustations accumulated on boiler surfaces through the creation of mechanical and thermal shock. Another approach is to use various types of soot blowers to spray cleaning materials through nozzles, which are located on the gas side of the boiler walls and/or on other heat exchange surfaces, where such soot blowers use any of the various media such as saturated steam, superheated steam, compressed air, water, etc., for removing soot from the boilers.
Soot blowing affects the efficiency and the expense of operating a fuel burning boiler. For example, if inadequate soot blowing is applied in a boiler, it results in excessive soot deposits on the surfaces of various steam carrying pipes and therefore in lower heat transfer rates. In some cases, inadequate soot blowing may result in “permanent fouling” within fuel burning boilers, meaning that soot deposits in the boiler are so excessive that such deposits cannot be removed by any additional soot blowing. In such a case, forced outage of the boiler operation may be required to fix the problem of excessive soot deposits, and boiler maintenance personnel may have to manually remove the soot deposits using hammers and chisels. Such forced outages are not only expensive, but also disruptive for the systems using such fuel burning boilers.
On the other hand, excessive soot blowing in fuel burning boilers may result in increased energy cost to operate the soot blowers, wastage of steam that could otherwise be used to operate turbines, etc. Excessive soot blowing may also be linked to boiler wall tube thinning, tube leaks, etc., which may cause forced outages of boiler use. Therefore, the soot blowing process needs to be carefully controlled.
Historically, soot blowing in utility boilers has been mostly an ad hoc practice, generally relying on a boiler operator's judgment. Such an ad hoc approach produces very inconsistent results. Therefore, it is important to manage the process of soot blowing more effectively and in a manner so that the efficiency of boiler operations is maximized and the cost associated with the soot blowing operations is minimized.
One popular method used for determining cleanliness of a boiler section and to control soot blowing operations is a first principle based method, which requires measurements of flue gas temperature and steam temperature at the boiler section inlets and outlets. However, because direct measurements of flue gas temperatures are not always available, the flue gas temperatures are often backward calculated at multiple points along the path of the flue gas, starting from the flue gas temperatures measured at an air heater outlet. This method is quite sensitive to disturbances and variations in air heater outlet flue gas temperatures, often resulting in incorrect results. Moreover, this method is a steady state method, and therefore does not work well in transient processes generally encountered in various boiler sections.
Another popular method used for determining cleanliness of a boiler section of a fuel burning boiler and to control soot blowing operations in a fuel burning boiler is an empirical model based method, which relies on an empirical model such as a neural network model, a polynomial fit model, etc. The empirical model based method generally requires a large quantity of empirical data related to a number of parameters, such as the fuel flow rate, the air flow rate, the air temperature, the water/steam temperature, the burner tilt, etc. Unfortunately the large amount of data makes the data collection process tedious and prone to high amount of errors in data collection. The model may also be similar to the performance monitoring method discussed above and using reference values and correction curves among other information from the manufacturer. As discussed above, this method evaluates the performance based on the manufacturer's design instead of the optimum achievable performance of the soot blowing operation under the current operating conditions.